I’ve got a long history of supporting and doing data-intensive science - I’ve done work in neuroscience, educational interventions, and pediatric interventions, and also worked to support campus-wide issues at UC Berkeley. I now work at Gigantum where we continue to develop the Gigantum Client and supporting cloud tools to make it easier for folks to share dockerized Jupyter and RStudio projects.
Whether or not you’re interested in Gigantum per se, I’ve been working on examples of how to organize covid-19 data and analyze it in a Gigantum Project:
You can just go to that URL and click through to launch and poke around without even having an account. If you sign up, you get like 5 free hours of compute a month (moving target ATM), and you can import from that URL to a local Gigantum client and do what you want - it’s your computer.
Right now, I’ve got the major data sources for US and global prevalence among other things, with some examples of how to work with the data. I’m going to share this kind of thing around and try to work more towards picking off actual issues that public health and related folks are asking for. My focus, though, is in facilitating accessible and easily auditable work.
Interesting observation, my home state of AZ has the highest percent increase over the last few days of states I’ve looked at! (Though prevalence is still quite low - and this is almost certainly confounded with uneven measurement error - it still gives me a sinking feeling.)
My strongest environments are the PyData and tidyverse stacks in python and R respectively, but I’m also happy to talk about bigger-picture strategy, data management, etc.